Pointers are the foundation of all hard interview problems, visualizing memory layout using pointers helps us understand what's really happening under the covers.

Practice is the key to understanding key pointer concepts, solve pointer problems by visualizing the memory layout.

Arrays are pointers at heart, work with them exactly like you would with pointers.

Pointers know how much space the data they point to occupies. Which means you can access all elements of an array with a single pointer, pointer arithmetic is pretty cool!

A whole bunch of practice with pointer problems. These should be easy because you will have the memory layout right there to help you visualize things.

Strings are character pointers which are equivalent to character arrays. Solve functions from the string.h library for practice dealing with pointers.

Pointers as arguments to functions have subtleties which need to be understood to use them correctly. Understand reassignment and modifications of pointers in a function and see how the original pointers are affected in the calling code.

Let's solve some harder problems from the string.h library, remember the little details like string termination, null inputs all matter in an interview!

Pointers just hold addresses of a memory location, which means we can have pointers to pointers to pointers. Sounds complicated? No worries, there are examples to help you understand these every step of the way.

Pointers to pointers requires a heightened conceptualization of memory layout. See detailed visuals on how pointer modification and reassignment work. User defined types or structs can also have pointers to them, memory layout and visualization of struct pointers.

Linked lists are favorite interview questions for software engineering roles. If you can work linked lists you're on your way to tackling more complicated problems. Understand the memory set up of linked lists and start with a few problems to gain confidence.

Get the length of a linked list, access the nth element in a list, and append an element to the end of the list - all this while handling null lists and other details.

For a simple concept, linked lists can get surprisingly tricky very quickly. Practice and practice again to gain mastery over linked list problems.

Use the linked list as a stack and implement pop, delete all the elements in a list - tricky memory freeing here, insert an element at a specified position and in a sorted list - these are similar but the edge cases differ.

Once you've actually solved all the examples we've got so far, you'll find that linked lists are fun! This class has a particularly difficult sample problem which will introduce you to the fast and slow pointers which traverse a linked list at different speeds. Useful for a lot of tricky maneuvering.

Append one list to another and split a list into two by using fast and slow pointers. This second problem is much, much harder than it seems.

By now you should be able to solve linked list problems in your sleep. Let's practice a few last ones to gain complete master over them.

Remove duplicates from a sorted list, move the first node from one list to another, merge two sorted lists and finally reverse a linked list.

Doubly linked lists lend themselves to fewer interesting problems. However its important to not overlook them, let's try deleting a node in a doubly linked list and see how to work with forward and reverse pointers.

We dig into the bitwise AND, OR and NOT operations - visually inspecting how they work.

We continue with bit manipulation - the right shift and left shift operators are very powerful, but they have 2 issues that you should be sure to understand: overflow, and fill.

Before diving headlong into bit manipulation problems it's helpful to learn a few useful tricks which help you build a strong foundation to visualize working with bits.

Functions to get the nth bit of an integer and to set the nth bit of an integer. These are the building block functions and the concepts underlying these will be used for harder bit manipulation problems.

Print all the bits used to represent an integer from the most significant bit to the least significant. Learn some subtle details about the shift right (>>) with negative numbers!

Count the number of 1s in an integer, and learn a neat trick which allows you to do it in complexity O(number of 1s).

Reverse the bits in an integer. This pulls together a whole bunch of stuff from the last few problems. As in the case of hard problems, visualizing the process is key to solving this!

During coding interviews you might encounter questions which you can work out from first principles. You should be nailing these! Let's start with figuring out whether a string is a palindrome and finding all the points within a certain distance from another point.

Two more problems and detailed solutions. Play the game of life where every cell can change states from live to dead based on its neighbours.

Then move on to breaking a document into chunks to send down to a client subject to very specific constraints.

Run length encoding involves specifying the number of times a character is repeated in a string. Decoding run-length-encoded strings can be pretty tricky, let's find a solution for both.

If a number were represented by its digits, can you write code to add 2 numbers represented in this way? Let's walk through a solution and see if you can get this right.

Write code to check whether a Sudoku board is valid. This should work for both complete and incomplete boards. Sudoku is tricky and this has many conditions to check.

Lastly set up your own numeric system and then increment a number represented in that system by 1.

What is the performance of your code? How do you measure this? What is complexity and what is its relationship with performance?

The Big O notation is used to express complexity based on the size of the input specified for any algorithm. How is Big O expressed, how is it calculated and many examples to drive the concepts home!

The Big O notation becomes much clearer when you practice find the complexity of some sample pieces of code. Let's see how many of these you get right!

A sorting algorithm is not just defined by its complexity, there are a whole bunch of other characteristics which can be used to determine which sorting algorithm is the right one for a system. Let's understand what these characteristics are and what are the trade offs we might make.

The simplest and most naive sorting algorithm.

Closely allied with selection sort is bubble sort. Its an adaptive sort with the same time complexity as selection sort.

Insertion sort is an improvement over both bubble sort and selection sort. Let's see how exactly it works and why it's preferred in many cases.

Shell sort builds on top of insertion sort, it improves the complexity of it's running time by partitioning the list in a clever way.

This belongs to a class of algorithms which uses divide and conquer to break the problem set into smaller pieces. This also makes a time-space trade off to get a faster running time.

Quick sort is the sort of choice for developers of programming libraries. Let's see what makes it so attractive.

Binary search is a pretty nifty way to search through a sorted list in O(Log N) time. It's also an interview favorite so make sure you understand it well!

Recursion is pretty hard at the beginning. Let's look at an example of reversing a string and see how we can use recursion to solve the problem. Visualize the input and every step and see how the magic of recursion works.

We've already seen and understood binary search. This is a perfect first problem to tackle using recursion. Make sure you try it yourself first before seeing the solutions in the class.

A classic problem which can be solved recursively. Try out a few smaller sets and see how their subsets look to find patterns.

Binary trees lend themselves to problems which have really beautiful recursive solutions. The problem may seem hard but the solutions end up being simple. Checking for whether 2 trees are the same is one such problem.

Paint fill allows you to color regions on screen while using drawing software. Implement a recursive solution to paint fill a region on the display screen.

Say you were given all the tasks needed to build a complete car. These tasks may depend on one another. Set up a data structure to represent a task and it's dependencies and write code to build a car.

An anagram of a word is simply a word with the letters of the original word rearranged. This is complicated but lends itself well to a recursive solution.

There can be several paths out of a maze. Help a rat placed anywhere in maze to find it's way out.

Another classic problem with an elegant recursive solution.

The stack is a very simple and easy to understand data structure. However it lies underneath many complicated real world problems and is incredibly useful.

Let's build a stack for real using Java. It'll have all the operations we're interested in - push, pop, peek, size etc. It can hold any data type, it's a generic class.

Problems which use stacks as a part of their solutions are very common in programming interviews. Matching parenthesis to check for well formed expressions is a classic interview question - let's solve this using the stack we're already implemented.

Another interview question implemented. You have space available but your processing needs to be very fast indeed. How would you keep track of the minimum element of a stack as it changes?

The queue belongs to the same linear data structure family as the stack but it's behavior is very different. Queues are much more intuitive as there are plenty of real world examples where a queue is the fair and correct way of processing.

A common, fast but slightly tricky implementation of the queue is the array where the last element wraps around to the first. An interview favorite, let's see how to implement the circular queue.

We know the stack, and we know the queue. This problem brings them together. It's possible to mimic the behavior of a queue using 2 stacks in the underlying implementation. Let's write the most efficient code possible to make this work.

The binary tree is an incredibly useful hierarchical data structure. Many other, more complex data structures, use the binary tree as the foundation. Let's see what a binary tree looks like and learn some simple terminology associated with the tree.

Traversing a binary tree can be done in variety of ways. The breadth first traversal visits and processes nodes at every level before moving on to the next. Let's visualize breadth first traversal and see how it's implemented.

Depth first traversal can be of 3 types based on the order in which the node is processed relative to it's left and right sub-trees. Pre-order traversal processes the node before processing the left and then the right sub trees.

Depth first traversal can be of 3 types based on the order in which the node is processed relative to it's left and right sub-trees.

In-order traversal processes the left subtree, then the node itself and then it's right sub trees. Post-order traversal processes the node *after* it's left and right subtrees.

The algorithms are all remarkably similar and very easy once you use recursion.

A Binary Search Tree is a binary tree with specific constraints which make it very useful in certain operations. Learn what a BST is and how we can use it

Insertion and Lookup are operations which are very fast in a Binary Search Tree. See how they work and understand their performance and complexity.

Find the minimum value in a binary search tree, find the maximum depth of a binary tree and mirror a binary tree. Learn to solve these problems recursively and see implementation details.

Count the number of structurally unique binary trees that can be built with N nodes, print the nodes within a certain range in a binary search tree and check whether a certain binary tree is a binary *search* tree. Learn to solve these problems and understand the implementation details.

Check if a path from root node to leaf node has a specified sum, print all paths from the root node to all leaf nodes and find the least common ancestor for two nodes in a binary tree. Learn to solve these problems and understand the implementation details.

Priority Queues allow us to make decisions about which task or job has the highest priority and has to be processed first. Common operations on a Priority Queue are insertion, accessing the highest priority element and removing the highest priority element.

The Binary Heap is the best implementation of the Priority Queue.

The Binary Heap is logically a Binary Tree with specific constraints. Constraints exist on the value of a node with respect to it's children and on the shape of the tree. The heap property and the shape property determine whether a Binary Tree is really a Heap.

The Binary Heap may logically be a tree, however the most efficient way to implement it is using an array. Real pointers from parent to child and from child to parent become implicit relationships on the indices of the array.

Let's build a real heap in Java!

How do we ensure that when we add an element or remove an element from an existing heap, that the heap property and shape property is maintained? This operation is called Heapify.

Once we understand heapify, adding and removing elements from a heap become very simple.

Back to sorting. The Heap Sort uses a heap to transform an unsorted array into a sorted array. Phase I is converting the unsorted array into a heap.

Phase II actually outputs the final sorted array. It involves removing the elements from the heap and placing it in a sorted array. The cool thing is that all of this can be done in-place.

Let's practice heap problems! Use the heap property to find the largest element in a minimum heap and the K largest elements in a stream.

An interview favorite - the streaming median! This uses heaps in a very interesting and elegant way.

Starting with K sorted lists, get one complete sorted list which includes all the elements from the original K lists. This is easy and efficient when done using heaps.

The graph is a data structure that is used to model a very large number of real world problems. It's also an programming interview favorite. The study of graphs and algorithms associated with graphs forms an entire field of study called graph theory.

Edges in a graph can be directed or undirected. A graph with directed edges forms a Directed Graph and those with undirected edges forms an Undirected Graph. These edges can be likened to one-way and two-way streets.

Different relationships can be modeled using either Directed or Undirected graphs. When a graph has no cycles it's called an acyclic graph. A graph with no cycles is basically a tree.

There are a number of different ways in which graphs can be implemented. However they all follow they same basic graph interface. The graph interface allows building up a graph by adding edges and traversing a graph by giving access to all adjacent vertices of any vertex.

An adjacency matrix is one way in which a graph can be represented. The graph vertices are rows and columns of the matrix and the cell value shows the relationship between the vertices of a graph.

The adjacency list and the adjacency set are alternate ways to represent a graph. Here the connection between the vertices is represented using either a linked list or a set.

Compare the adjacency matrix, adjacency list and the adjacency set in terms of space and time complexity of common operations

Common traversal methods of trees apply to graphs as well. There is an additional wrinkle with graphs, dealing with cycles and with unconnected graphs. Otherwise the algorithms are exactly the same as those we use to traverse trees.

Topological sort is an ordering of vertices in a graph where a vertex comes before every other vertex to which it has outgoing edges? A mouthful? This lecture will make things easy to follow. Topological sort is widely used in real world problems.

Here is the code in Java to implement topological sort.